Importing all necessary libraries

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.io as pio
pio.renderers.default = 'notebook'  

Loading the Data

pesticides=pd.read_csv('pesticides.csv')
yield_df=pd.read_csv('yield_df.csv')

Exploratory Data Analysis

Top ten Countries which used the highest amount of pesticides in the year of 2016

ten_pesticides_country=pesticides[pesticides.Year==2016][['Area','Value']].sort_values(by='Value',ascending=False).head(10)

fig=px.bar(ten_pesticides_country,
           y='Area',
           x='Value',
           title='Top ten countrues which used the highest amount of pesticides in 2016',
           color=ten_pesticides_country.Area)
fig.update_layout(
    yaxis_title='Country',
    xaxis_title='Tonnes of active ingredients',
    width=1200,
    height=600
)
fig.show()

Pesticides usage in China (Different regions) from 1990 to 2016

Mainland of China used the highest amount of Pesticides compared to other regions

pesticides_china=pesticides[pesticides.Area.isin(['China, Hong Kong SAR', 'China, Macao SAR','China, mainland', 'China, Taiwan Province of'])][['Area','Year','Value']]

fig=px.bar(
    pesticides_china,
    x='Year',
    y='Value',
    color='Area',
    title='Pesticides Usage in China from 1990 to 2016',
)
fig.update_layout(
    yaxis_title='Tonnes of active ingredients',
    width=1200,
    height=600
)
fig.show()

Comparisions Pesticides usage in China between Hong Kong SAR, Macao SAR

Hong Kong SAR used more pesticides than Macao SAR

pest_china_other_area=pesticides[pesticides.Area.isin(['China, Hong Kong SAR', 'China, Macao SAR','China'])]

fig=px.bar(
    pest_china_other_area,
    x='Year',
    y='Value',
    color='Area',
    title='Pesticides usage in China (Hong Kong SAR, Macao SAR)'
)
fig.update_layout(
    yaxis_title='Tonnes of Active Ingredients',
    width=1200,
    height=600
)
fig.show()

Pesticides usage in Bangladesh from 1990 to 2016

Pesticide usage significantly increased in BD in those years

pesticides_bd=pesticides[pesticides.Area=='Bangladesh']

fig=px.bar(pesticides_bd,
           x='Year',
           y='Value',
           color='Value',
           title='Pesticides Usage in Bangladesh From 1990 to 2016')

fig.update_layout(yaxis_title='Tonnes of active ingredients',
                  width=1200,
                  height=600)
fig.show()

Analysis of pesticide usage and the yield of some dominent crops in Bangladesh from 1990 to 2013

Application of pesticides was significantly higher for Potato compared to other crops

There was some sort of uneven distribution of pesticides and the yield of crops from 2008 to 2012

yield_df=yield_df.drop(columns='Unnamed: 0',inplace=False,axis=1)
yield_df['hg/ha_yield']=yield_df['hg/ha_yield']/10000
yield_df=yield_df.rename(columns={'hg/ha_yield':'ton/ha_yield'})
yield_bd=yield_df[yield_df.Area=='Bangladesh']

fig=px.line(yield_bd,
            x='pesticides_tonnes',
            y=yield_bd['ton/ha_yield'],
            hover_data=['Year'],
            color='Item',
            title='Pesticides usage on the yield of some dominent crops in Bangladesh from 1990 to 2013')

fig.update_layout(
    yaxis_title='Yield (Tonne/Ha)',
    xaxis_title='Pesticides Dose (Tonnes)',
    width=1200,
    height=700
)
fig.show()

Pesticides usage and yield of Rice in Bangladesh from 1990 to 2013

yield_bd_rice=yield_bd[yield_bd.Item=='Rice, paddy']

fig=px.line(yield_bd_rice,
            x='pesticides_tonnes',
            y='ton/ha_yield',
            title='Effects of pesticides usage on Rice yield in Bangladesh from 1990 to 2013',
            hover_data=['Year']
            )
fig.update_layout(
    yaxis_title='Yield (Tonne/Ha)',
    xaxis_title='Pesticides Dose (Tonnes)',
    width=1200,
    height=600
)
fig.show()

Both the yield of crops and pesticides application from 2008 to 2012 was uneven

yield_8_9_10_11_12=yield_df[yield_df.Year.isin(range(2008,2013))]
yield_8_9_10_11_12_bd=yield_8_9_10_11_12[yield_8_9_10_11_12.Area=='Bangladesh']
yield_pests_8to12=yield_8_9_10_11_12_bd.groupby('Year')[['ton/ha_yield','pesticides_tonnes']].sum()

fig=px.bar(yield_pests_8to12,y='pesticides_tonnes',
            x=yield_pests_8to12.index,
            title='Pesticides usage and yield of crops from 2008 to 20012',
            color='ton/ha_yield'
            )
fig.update_layout(
    yaxis_title='Pesticides Dose (Tonnes)',
    xaxis_title='Year',
    width=1200,
    height=600
)
fig.show()

yield_2000to2007=yield_df[yield_df.Year.isin([2000,2001,2002,2004,2005,2006,2007])]
yield_2000to2007bd=yield_2000to2007[yield_2000to2007.Area=='Bangladesh']
yield_2000to2007bd=yield_2000to2007bd.groupby('Year')[['ton/ha_yield','pesticides_tonnes']].sum()

However, the distribution ascended in some of the previous years (2000 to 2007)

fig=px.bar(yield_2000to2007bd,y='pesticides_tonnes',
            x=yield_2000to2007bd.index,
            title='Pesticides usage from 2000 to 2007 in BD',
            color='ton/ha_yield'
            )
fig.update_layout(
    yaxis_title='Pesticides dose (Tonnes)',
    xaxis_title='Year',
    width=1200,
    height=600
    )

Some Basic analysis

Top ten maize Producing Countries in 2013

crop_count=yield_df.groupby('Area')['ton/ha_yield'].sum()
maize_df=yield_df[yield_df.Item=='Maize'][['Area','Year','Item','ton/ha_yield']].sort_values(by='ton/ha_yield',ascending=False)
maize_df_13=maize_df[maize_df.Year==2013]
top_ten_maize_df_13=maize_df_13.groupby('Area')['ton/ha_yield'].sum().sort_values(ascending=False).head(10)

fig=px.bar(
    top_ten_maize_df_13,
    x=top_ten_maize_df_13.index,
    y=top_ten_maize_df_13,
    title='Top ten Maize producing countries in 2013',
    color=top_ten_maize_df_13.index
)
fig.update_layout(
    xaxis_title='Country',
    yaxis_title='Yield (Tonne/Ha)',
    width=1200,
    height=600
)
fig.show()

Top ten Rice Producing Countries in 2013

rice_df=yield_df[yield_df.Item=='Rice, paddy']
rice_df=rice_df[rice_df.Year==2013]
rice_df
top_ten_rice_df_13=rice_df.groupby('Area')['ton/ha_yield'].sum()
top_ten_rice_df_13=top_ten_rice_df_13.sort_values(ascending=False)
top_ten_rice_df_13=top_ten_rice_df_13.head(10)

fig=px.bar(
    top_ten_rice_df_13,
    x=top_ten_rice_df_13.index,
    y=top_ten_rice_df_13,
    title='Top ten Rice producing countries in 2013',
    color=top_ten_rice_df_13.index
)
fig.update_layout(
    xaxis_title='Country',
    yaxis_title='Yield (Tonne/Ha)',
    width=1200,
    height=600
)
fig.show()

Ten highest crops (Maize, Potatoes, Rice, Sorghum, Soybeans, Wheat, Cassava, Sweet potatoes, Plantains and others, Yams) yielding countries from 1990 to 2013

highest_crops_yield=yield_df.groupby('Area')['ton/ha_yield'].sum()
highest_crops_yield=highest_crops_yield.sort_values(ascending=False)
ten_highest_crops_yield=highest_crops_yield.head(10)

fig=px.bar(
    ten_highest_crops_yield,
    x=ten_highest_crops_yield.index,
    y=ten_highest_crops_yield,
    title='Ten highest crop yielding Countries (1990 to 2013)',
    color=ten_highest_crops_yield.index
)

fig.update_layout(
xaxis_title='Country',
yaxis_title='Yield (Tonne/Ha)',
width=1200,
height=600
)
fig.show()